Response Modeling with Support Vector Machines

Shin, H; Cho, S

doi:10.1016/j.eswa.2005.07.037

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

Response Modeling with Support Vector Machines

MPS-Authors

/persons/resource/persons84217

Shin, H
Friedrich Miescher Laboratory, Max Planck Society;

External Resource

https://www.sciencedirect.com/science/article/pii/S0957417405001715
(出版社版)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Shin, H., & Cho, S. (2006). Response Modeling with Support Vector Machines. Expert Systems with Applications, 30(4), 746-760. doi:10.1016/j.eswa.2005.07.037.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-D1D7-B

要旨

Support Vector Machine (SVM) employs Structural Risk minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization
(ERM) principle. When applying SVM to response modeling in direct marketing,h owever,one has to deal with the practical difficulties:
large training data,class imbalance and binary SVM output. This paper proposes ways to alleviate or solve the addressed difficulties through informative sampling,u se of different costs for different classes, and use of distance to decision boundary. This paper also provides various
evaluation measures for response models in terms of accuracies,lift chart analysis and computational efficiency.